Information retrieval systems, generally called search engines, are now an essential tool for finding information in large scale, diverse, and growing corpuses such as the Internet. Generally, search engines create an index that relates documents (or “pages”) to the individual words present in each document. A document is retrieved in response to a query containing a number of query terms, typically based on having some number of query terms present in the document. The retrieved documents are then ranked according to other statistical measures, such as frequency of occurrence of the query terms, host domain, link analysis, and the like. The retrieved documents are then presented to the user, typically in their ranked order, and without any further grouping or imposed hierarchy. In some cases, a selected portion of a text of a document is presented to provide the user with a glimpse of the document's content.
Direct “Boolean” matching of query terms has well known limitations, and in particular does not identify documents that do not have the query terms, but have related words. For example, in a typical Boolean system, a search on “Australian Shepherds” would not return documents about other herding dogs such as Border Collies that do not have the exact query terms. Rather, such a system is likely to also retrieve and highly rank documents that are about Australia (and have nothing to do with dogs), and documents about “shepherds” generally.
The problem here is that conventional systems index documents based on individual terms, than on concepts. Concepts are often expressed in phrases, such as “Australian Shepherd,” “President of the United States,” or “Sundance Film Festival”. At best, some prior systems will index documents with respect to a predetermined and very limited set of ‘known’ phrases, which are typically selected by a human operator. Indexing of phrases is typically avoided because of the perceived computational and memory requirements to identify all possible phrases of say three, four, or five or more words. For example, on the assumption that any five words could constitute a phrase, and a large corpus would have at least 200,000 unique terms, there would approximately 3.2×1026 possible phrases, clearly more than any existing system could store in memory or otherwise programmatically manipulate. A further problem is that phrases continually enter and leave the lexicon in terms of their usage, much more frequently than new individual words are invented. New phrases are always being generated, from sources such technology, arts, world events, and law. Other phrases will decline in usage over time.
Some existing information retrieval systems attempt to provide retrieval of concepts by using co-occurrence patterns of individual words. In these systems a search on one word, such as “President” will also retrieve documents that have other words that frequently appear with “President”, such as “White” and “House.” While this approach may produce search results having documents that are conceptually related at the level of individual words, it does not typically capture topical relationships that inhere between co-occurring phrases.
Accordingly, there is a need for an information retrieval system and methodology that can comprehensively identify phrases in a large scale corpus, index documents according to phrases, search and rank documents in accordance with their phrases, and provide additional clustering and descriptive information about the documents.